Concurrent 22. Presentation for: Intelligent monitoring of fugitive emissions – comparison of continuous monitoring with intelligent analytics to other emissions monitoring technologies
Bibliographic record
Abstract
Presented on Thursday 19 May: Session 22 Studies have shown that fugitive emissions are dominated by a small number of sources with extremely high emission rates, known as super-emitters. These super-emitters present an opportunity to significantly reduce emissions in a cost-effective manner if they are managed effectively. This requires the ability to detect, locate, and accurately measure emissions. However, the uncertain nature of fugitive emissions presents challenges to monitoring. Existing and emerging technologies enable emissions management with varying levels of success. This paper provides a practical comparison of several fugitive emissions monitoring technologies, including handheld gas detectors, optical gas imaging cameras, vehicle-based systems, satellites, aircraft, and unmanned aerial vehicles. These technologies provide periodic monitoring of a facility and are compared to continuous monitoring technologies that monitor emissions on a 24/7 basis using fixed sensors and advanced analytics to identify and track emission plumes. Continuous monitoring with intelligent analytics has demonstrated great potential in overcoming the challenges of monitoring fugitive emissions to reduce greenhouse gases and other problematic emissions. Features, capabilities, and limitations of these technologies are explored in the context of gas facilities, including their ability to detect intermittent sources, identify unsuspected and off-site sources, and quantify emissions. The range of monitoring for each technology and safety concerns associated with their use are discussed. To access the presentation click the link on the right. To read the full paper click here
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".